Dynamic Node Selection Research Articles

A cluster-based quantum key distribution with dynamic node selection: an improved approach for scalability and security in quantum communication

A cluster-based quantum key distribution with dynamic node selection: an improved approach for scalability and security in quantum communication

Dynamic Node Selection Approach for 6G Heterogeneous Cellular Networks (HCN) to Enhance QoS and Low Latency

A wireless multi-hop network is a group of nodes that can interact directly with one another or across longer distances by using other nodes as relay points and routers. 6G heterogeneous cellular network support several transmission modalities, such as single hop, multi-hop transmission and device-to-any device transmission. These transmission services can greatly increase 6G cellular network capacity, spectral efficiency, power efficiency, Quality of Service (QoS) and Ultra Reliable Low Latency Communication (URLLC). According to current research, Heterogeneous Cellular Networks (HCN) can benefit even more from the integration of demand-driven opportunistic networking into HCN. Devices can search look for the connections between nodes that are most efficient. in Proactive HCN connections by taking advantage of the URLLC acceptability of many mobile data services. Multiple Transmission modes necessitate node selection schemes that can determine the best node for each transmission. Previous proposals for node selection schemes have taken into account the introduction of D2X and HCN. Proactive HCN connections, however, cannot be incorporated into the selection process by current node selection schemes. By implementing outward the first node selection scheme that can incorporate Proactive HCN sharing information within 6G and beyond networks, this research work enhances the existing work. The analysis that was carried out shows the potential for Proactive HCN communications as well as the ability of the suggested node selection scheme to choose the best communication node to enhance QoS and Low Latency.

Research on an Evaluation Algorithm of Sensing Node Reliability in Cognitive Networks

In multi-node cooperative sensing of cognitive networks, as the number of nodes increases, and the energy consumption must increase, but the sensing performance does not necessarily improve. The nodes with less information are not helpful for the sensing performance but will increase the unnecessary energy consumption. To improve the sensing performance and reduce the energy consumption of nodes, a dynamic node selection algorithm based on reinforcement learning is proposed in this paper. The algorithm can evaluate the reliability of sensing nodes in real-time, select the nodes with the highest reliability to participate in cooperative sensing, and update the reliability of nodes in real-time through the method of combining feedback energy consumption and sensing performance. In a real-time environment, nodes with high reliability are selected to participate in cooperative sensing, and the optimal balance between sensing performance and energy consumption is achieved. The experimental results show that the proposed algorithm can reduce energy consumption and improve the perception performance at the same time. Under the same conditions, the detection probability is 5% higher than that of the traditional method, while the energy consumption is only 16.7% of that of the traditional method.

Optimal path selection algorithm for mobile beacons in sensor network under non-dense distribution

Abstract When the traditional anchor aided location algorithm is used to select the mobile beacon path in the sensor network, there is no analysis of the energy imbalance of nodes in non-dense conditions, the optimal network node cannot be selected, and the selection error of the optimal path of the beacon is larger. A path selection algorithm for mobile beacons in a sensor network under non-dense distribution is proposed. Using the mobile beacon based wireless sensor network location algorithm, the weighted centroid algorithm and the extended Kalman filter (EKF) are used to obtain the accurate location results of the unknown nodes around the mobile beacon in the sensor network under non-dense distribution condition. The optimal node energy partition of the unknown node is obtained by the chaotic differential evolution method, and the optimal location of the optimal energy node in the wireless sensor network is calculated using the dynamic escape particle swarm optimization method, and the optimal beacon path is extracted. The experimental results show that the proposed algorithm can enhance the clustering performance of the optimal node in the wireless sensor network and has a better performance of dynamic node selection in wireless sensor network, and the convergence speed is faster and the operation time is shorter.

Dynamic node selection in camera networks based on approximate reinforcement learning

In camera networks, dynamic node selection is an effective technique that enables video stream transmission with constrained network bandwidth, more economical node cooperation for nodes with constrained power supplies, and optimal use of a limited number of display terminals, particularly for applications that need to obtain high-quality video of specific targets. However, the nearest camera in a network cannot be identified by directional measurements alone. Furthermore, errors are introduced into computer vision algorithms by complex background, illumination, and other factors, causing unstable and jittery processing results. Consequently, in selecting camera network nodes, two issues must be addressed: First, a dynamic selection mechanism that can choose the most appropriate node is needed. Second, metrics to evaluate the visual information in a video stream must be modeled and adapted to various camera parameters, backgrounds, and scenes. This paper proposes a node selection method based on approximate reinforcement learning in which nodes are selected to obtain the maximum expected reward using approximate Q-learning. The Q-function is approximated by a Gaussian Mixture Model with parameters that are sequentially updated by a mini-batch stepwise Expectation---Maximization algorithm. To determine the most informative camera node dynamically, the immediate reward in Q-learning integrates the visibility, orientation, and image clarity of the object in view. Experimental results show that the proposed visual evaluation metrics can effectively capture the motion state of objects, and that the selection method reduces camera switching and related errors compared with state-of-the art methods.

Energy-Efficient Node Selection for Target Tracking in Wireless Sensor Networks

Saving energy while preserving accuracy is of paramount importance to target tracking in wireless sensor networks. This paper presents an energy-efficient selection of cooperative nodes. In the proposed method, the target detection probability is estimated by single-node processing based on particle filter. Then, an objective function for collaborative target tracking in wireless sensor networks is constructed according to the information utility and the remaining energy of sensor nodes. With this understanding, a dynamic node selection scheme based on genetic algorithms is proposed, which can optimize the tradeoff between the accuracy of tracking and the energy cost of nodes. Simulations demonstrate its superior performance in estimating the target location and saving sensor nodes energy.

CDSWS: coverage-guaranteed distributed sleep/wake scheduling for wireless sensor networks

Minimizing the energy consumption of battery-powered sensors is an essential consideration in sensor network applications, and sleep/wake scheduling mechanism has been proved to an efficient approach to handling this issue. In this article, a coverage-guaranteed distributed sleep/wake scheduling scheme is presented with the purpose of prolonging network lifetime while guaranteeing network coverage. Our scheme divides sensor nodes into clusters based on sensing coverage metrics and allows more than one node in each cluster to keep active simultaneously via a dynamic node selection mechanism. Further, a dynamic refusal scheme is presented to overcome the deadlock problem during cluster merging process, which has not been specially investigated before. The simulation results illustrate that CDSWS outperforms some other existed algorithms in terms of coverage guarantee, algorithm efficiency and energy conservation.

A Hierarchical Identity Based Key Management Scheme in Tactical Mobile Ad Hoc Networks

Hierarchical key management schemes would serve well for military applications where the organization of the network is already hierarchical in nature. Most of the existing key management schemes concentrate only on network structures and key allocation algorithms, ignoring attributes of the nodes themselves. Due to the distributed and dynamic nature of MANETs, it is possible to show that there is a security benefit to be attained when the node states are considered in the process of constructing a private key generator (PKG). In this paper, we propose a distributed hierarchical key management scheme in which nodes can get their keys updated either from their parent nodes or a threshold of sibling nodes. The dynamic node selection process is formulated as a stochastic problem and the proposed scheme can select the best nodes to be used as PKGs from all available ones considering their security conditions and energy states. Simulation results show that the proposed scheme can decrease network compromising probability and increase network lifetime in tactical MANETs.